Ubuntu

Install Apache Kafka 4.2 on Ubuntu WSL2 (KRaft)

This technical guide explains how to install and run Apache Kafka 4.2 in KRaft (Kafka Raft Metadata) mode on Ubuntu 24.04 running under WSL2. It covers prerequisites (Java 11+), downloading and extracting Kafka, configuring server.properties for a single-node broker+controller, initializing KRaft metadata (generating a Cluster ID and running kafka-storage.sh format), starting the broker, creating/listing/describing topics, producing and consuming messages, and common administrative commands and troubleshooting. The author highlights the removal of ZooKeeper in favor of KRaft, the importance of the controller.quorum.voters setting, and the two-listener port model (9092 for clients, 9093 for internal controller communication).

Hardware-Accelerated FFmpeg on NVIDIA Jetson AGX Orin

This technical guide explains how to compile FFmpeg with hardware acceleration (NVENC/NVDEC) for the NVIDIA Jetson AGX Orin 64GB running Ubuntu 22.04 LTS and JetPack 6.2.2 (CUDA 12.6). It walks through installing system dependencies and the NVIDIA codec headers, cloning and configuring FFmpeg with explicit CUDA/NVENC/NVDEC flags, compiling (recommended make -j12 on the 12-core AGX Orin), and verifying available NV encoders/decoders. The article compares use cases and performance trade-offs across FFmpeg (custom build), NVIDIA DeepStream (for real-time AI inference with zero-copy/TensorRT), GStreamer (L4T plugins), and CUDA‑compiled OpenCV, and gives architectural recommendations on when to use each tool for edge video, streaming, and AI inference scenarios.

Lessons from Building an Enterprise AI SaaS Platform

A developer recounts practical lessons from building an AI‑powered enterprise SaaS platform, arguing the hardest work is not calling an LLM but operationalizing AI within real business environments. Core areas that expand into full architectural systems include API key management (scopes, revocation, tenant boundaries), SSO and trust decisions for multi-tenant identity, AI usage metering (token consumption, provider/model tracking, cost visibility), billing tied to product plans and deployment modes, Kubernetes-based execution architecture and workload separation, and observability as a product requirement. The piece emphasizes that these subsystems are tightly coupled — weaknesses in one area (for example, billing or SSO) compromise the whole platform’s ability to scale safely and reliably.